Hierarchical Neural Architecture Search for Single Image Super-Resolution

• URL: http://arxiv.org/abs/2003.04619v3
• Date: Tue, 16 Jun 2020 12:43:24 GMT
• Title: Hierarchical Neural Architecture Search for Single Image Super-Resolution
• Authors: Yong Guo, Yongsheng Luo, Zhenhao He, Jin Huang, Jian Chen
• Abstract summary: Deep neural networks have exhibited promising performance in image super-resolution (SR) Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. We propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost.
• Score: 18.624661846174412
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks have exhibited promising performance in image super-resolution (SR). Most SR models follow a hierarchical architecture that contains both the cell-level design of computational blocks and the network-level design of the positions of upsampling blocks. However, designing SR models heavily relies on human expertise and is very labor-intensive. More critically, these SR models often contain a huge number of parameters and may not meet the requirements of computation resources in real-world applications. To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost. To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search. Such a hierarchical controller is able to simultaneously find promising cell-level blocks and network-level positions of upsampling layers. Moreover, to design compact architectures with promising performance, we build a joint reward by considering both the performance and computation cost to guide the search process. Extensive experiments on five benchmark datasets demonstrate the superiority of our method over existing methods.

Related papers

• EM-DARTS: Hierarchical Differentiable Architecture Search for Eye Movement Recognition [54.99121380536659]
  Eye movement biometrics have received increasing attention thanks to its high secure identification. Deep learning (DL) models have been recently successfully applied for eye movement recognition. DL architecture still is determined by human prior knowledge. We propose EM-DARTS, a hierarchical differentiable architecture search algorithm to automatically design the DL architecture for eye movement recognition.
  arXiv  Detail & Related papers  (2024-09-22T13:11:08Z)
• Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
  We identify and test new hybrid architectures constructed from a variety of computational primitives. We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis. We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
  arXiv  Detail & Related papers  (2024-03-26T16:33:12Z)
• Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric [12.52012450501367]
  Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to automatically design the architecture of a neural network for a specific task and target hardware. evaluating the performance of candidate architectures is a key challenge in HW-NAS, as it requires significant computational resources. We propose an efficient hardware-aware evolution-based NAS approach called HW-EvRSNAS.
  arXiv  Detail & Related papers  (2023-11-07T11:58:40Z)
• HKNAS: Classification of Hyperspectral Imagery Based on Hyper Kernel Neural Architecture Search [104.45426861115972]
  We propose to directly generate structural parameters by utilizing the specifically designed hyper kernels. We obtain three kinds of networks to separately conduct pixel-level or image-level classifications with 1-D or 3-D convolutions. A series of experiments on six public datasets demonstrate that the proposed methods achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-04-23T17:27:40Z)
• Efficient Search of Multiple Neural Architectures with Different Complexities via Importance Sampling [3.759936323189417]
  This study focuses on the architecture complexity-aware one-shot NAS that optimize the objective function composed of the weighted sum of two metrics. The proposed method is applied to the architecture search of convolutional neural networks on the CIAFR-10 and ImageNet datasets.
  arXiv  Detail & Related papers  (2022-07-21T07:06:03Z)
• SuperNet in Neural Architecture Search: A Taxonomic Survey [14.037182039950505]
  This survey focuses on the supernet optimization that builds a neural network that assembles all the architectures as its sub models by using weight sharing. We aim to accomplish that by proposing them as solutions to the common challenges found in the literature: data-side optimization, poor rank correlation alleviation, and transferable NAS for a number of deployment scenarios.
  arXiv  Detail & Related papers  (2022-04-08T08:29:52Z)
• Rethinking Architecture Selection in Differentiable NAS [74.61723678821049]
  Differentiable Neural Architecture Search is one of the most popular NAS methods for its search efficiency and simplicity. We propose an alternative perturbation-based architecture selection that directly measures each operation's influence on the supernet. We find that several failure modes of DARTS can be greatly alleviated with the proposed selection method.
  arXiv  Detail & Related papers  (2021-08-10T00:53:39Z)
• Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search [38.83764580480486]
  Single Image Super-Resolution (SISR) tasks have achieved significant performance with deep neural networks. We propose a novel differentiable Neural Architecture Search (NAS) approach on both the cell-level and network-level to search for lightweight SISR models.
  arXiv  Detail & Related papers  (2021-05-09T13:30:16Z)
• Stage-Wise Neural Architecture Search [65.03109178056937]
  Modern convolutional networks such as ResNet and NASNet have achieved state-of-the-art results in many computer vision applications. These networks consist of stages, which are sets of layers that operate on representations in the same resolution. It has been demonstrated that increasing the number of layers in each stage improves the prediction ability of the network. However, the resulting architecture becomes computationally expensive in terms of floating point operations, memory requirements and inference time.
  arXiv  Detail & Related papers  (2020-04-23T14:16:39Z)
• RC-DARTS: Resource Constrained Differentiable Architecture Search [162.7199952019152]
  We propose the resource constrained differentiable architecture search (RC-DARTS) method to learn architectures that are significantly smaller and faster. We show that the RC-DARTS method learns lightweight neural architectures which have smaller model size and lower computational complexity.
  arXiv  Detail & Related papers  (2019-12-30T05:02:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.